all mpnet base (onnx)

all-mpnet-base-v1-onnx/.gitattributes

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all-mpnet-base-v1-onnx/README.md

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+---
+library_name: light-embed
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# onnx-models/all-mpnet-base-v1-onnx
+
+This is the ONNX-ported version of the [sentence-transformers/all-mpnet-base-v1](https://huggingface.co/sentence-transformers/all-mpnet-base-v1) for generating text embeddings.
+
+## Model details
+- Embedding dimension: 768
+- Max sequence length: 512
+- File size on disk:  0.41 GB
+- Modules incorporated in the onnx: Transformer, Pooling, Normalize
+
+<!--- Describe your model here -->
+
+## Usage
+
+Using this model becomes easy when you have [light-embed](https://pypi.org/project/light-embed/) installed:
+
+```
+pip install -U light-embed
+```
+
+Then you can use the model by specifying the *original model name* like this:
+
+```python
+from light_embed import TextEmbedding
+sentences = [
+	"This is an example sentence",
+	"Each sentence is converted"
+]
+
+model = TextEmbedding('sentence-transformers/all-mpnet-base-v1')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+or by specifying the *onnx model name* like this:
+
+```python
+from light_embed import TextEmbedding
+sentences = [
+	"This is an example sentence",
+	"Each sentence is converted"
+]
+
+model = TextEmbedding('onnx-models/all-mpnet-base-v1-onnx')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Citing & Authors
+
+Binh Nguyen / binhcode25@gmail.com

all-mpnet-base-v1-onnx/config.json

@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v1",
+  "architectures": [
+    "MPNetForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "transformers_version": "4.8.2",
+  "vocab_size": 30527
+}

all-mpnet-base-v1-onnx/model.onnx

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all-mpnet-base-v1-onnx/source.txt

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+https://huggingface.co/onnx-models/all-mpnet-base-v1-onnx

all-mpnet-base-v1-onnx/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

all-mpnet-base-v1-onnx/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

all-mpnet-base-v2-fine-tuned-epochs-1-onnx/.gitattributes

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all-mpnet-base-v2-fine-tuned-epochs-1-onnx/README.md

@@ -0,0 +1,61 @@
+---
+library_name: light-embed
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# onnx-models/all-mpnet-base-v2-fine-tuned-epochs-1-onnx
+
+This is the ONNX-ported version of the [event-nlp/all-mpnet-base-v2-fine-tuned-epochs-1](https://huggingface.co/event-nlp/all-mpnet-base-v2-fine-tuned-epochs-1) for generating text embeddings.
+
+## Model details
+- Embedding dimension: 768
+- Max sequence length: 384
+- File size on disk:  0.41 GB
+- Modules incorporated in the onnx: Transformer, Pooling, Normalize
+
+<!--- Describe your model here -->
+
+## Usage
+
+Using this model becomes easy when you have [light-embed](https://pypi.org/project/light-embed/) installed:
+
+```
+pip install -U light-embed
+```
+
+Then you can use the model by specifying the *original model name* like this:
+
+```python
+from light_embed import TextEmbedding
+sentences = [
+	"This is an example sentence",
+	"Each sentence is converted"
+]
+
+model = TextEmbedding('event-nlp/all-mpnet-base-v2-fine-tuned-epochs-1')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+or by specifying the *onnx model name* like this:
+
+```python
+from light_embed import TextEmbedding
+sentences = [
+	"This is an example sentence",
+	"Each sentence is converted"
+]
+
+model = TextEmbedding('onnx-models/all-mpnet-base-v2-fine-tuned-epochs-1-onnx')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Citing & Authors
+
+Binh Nguyen / binhcode25@gmail.com

all-mpnet-base-v2-fine-tuned-epochs-1-onnx/config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "event-nlp/all-mpnet-base-v2-fine-tuned-epochs-1",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.32.1",
+  "vocab_size": 30527
+}

all-mpnet-base-v2-fine-tuned-epochs-1-onnx/model.onnx

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all-mpnet-base-v2-fine-tuned-epochs-1-onnx/source.txt

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+https://huggingface.co/onnx-models/all-mpnet-base-v2-fine-tuned-epochs-1-onnx

all-mpnet-base-v2-fine-tuned-epochs-1-onnx/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

all-mpnet-base-v2-fine-tuned-epochs-1-onnx/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    },
+    "30526": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
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+      "special": true
+    }
+  },
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
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+  "model_max_length": 384,
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+  "pad_token": "<pad>",
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+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

all-mpnet-base-v2-fine-tuned-epochs-8-onnx/.gitattributes

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all-mpnet-base-v2-fine-tuned-epochs-8-onnx/README.md

@@ -0,0 +1,61 @@
+---
+library_name: light-embed
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# onnx-models/all-mpnet-base-v2-fine-tuned-epochs-8-onnx
+
+This is the ONNX-ported version of the [event-nlp/all-mpnet-base-v2-fine-tuned-epochs-8](https://huggingface.co/event-nlp/all-mpnet-base-v2-fine-tuned-epochs-8) for generating text embeddings.
+
+## Model details
+- Embedding dimension: 768
+- Max sequence length: 384
+- File size on disk:  0.41 GB
+- Modules incorporated in the onnx: Transformer, Pooling, Normalize
+
+<!--- Describe your model here -->
+
+## Usage
+
+Using this model becomes easy when you have [light-embed](https://pypi.org/project/light-embed/) installed:
+
+```
+pip install -U light-embed
+```
+
+Then you can use the model by specifying the *original model name* like this:
+
+```python
+from light_embed import TextEmbedding
+sentences = [
+	"This is an example sentence",
+	"Each sentence is converted"
+]
+
+model = TextEmbedding('event-nlp/all-mpnet-base-v2-fine-tuned-epochs-8')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+or by specifying the *onnx model name* like this:
+
+```python
+from light_embed import TextEmbedding
+sentences = [
+	"This is an example sentence",
+	"Each sentence is converted"
+]
+
+model = TextEmbedding('onnx-models/all-mpnet-base-v2-fine-tuned-epochs-8-onnx')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Citing & Authors
+
+Binh Nguyen / binhcode25@gmail.com

all-mpnet-base-v2-fine-tuned-epochs-8-onnx/config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "event-nlp/all-mpnet-base-v2-fine-tuned-epochs-8",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.32.1",
+  "vocab_size": 30527
+}

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all-mpnet-base-v2-fine-tuned-epochs-8-onnx/source.txt

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+https://huggingface.co/onnx-models/all-mpnet-base-v2-fine-tuned-epochs-8-onnx

all-mpnet-base-v2-fine-tuned-epochs-8-onnx/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

all-mpnet-base-v2-fine-tuned-epochs-8-onnx/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

all-mpnet-base-v2-onnx/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

all-mpnet-base-v2-onnx/README.md

@@ -0,0 +1,69 @@
+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- ONNX
+- Optimum
+- ONNXRuntime
+inference: false
+
+---
+# ONNX version of `sentence-transformers/all-mpnet-base-v2`
+
+This is the ONNX version of https://huggingface.co/sentence-transformers/all-mpnet-base-v2, examined that the produced embeddings are the same.
+
+Optmized for CPU usage.
+
+## Convert
+
+The same checkpoint can also be created by using the `convert.py` script.
+
+## Usage - `transformers`
+
+Exactly the same as in `sentence-transformers/all-mpnet-base-v2` except using `ORTModelForFeatureExtraction` from optimum.
+
+```bash
+pip install optimum[onnxruntime]
+```
+
+```python
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+import torch
+import torch.nn.functional as F
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('yilunzhang/all-mpnet-base-v2-onnx')
+model = ORTModelForFeatureExtraction.from_pretrained('yilunzhang/all-mpnet-base-v2-onnx')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```

all-mpnet-base-v2-onnx/config.json

@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "microsoft/mpnet-base",
+  "architectures": [
+    "MPNetForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "transformers_version": "4.8.2",
+  "vocab_size": 30527
+}

all-mpnet-base-v2-onnx/convert.py

@@ -0,0 +1,22 @@
+from transformers import AutoModel
+import torch
+
+max_seq_length = 384
+
+model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2")
+model.eval()
+
+inputs = {
+    "input_ids": torch.ones(1, max_seq_length, dtype=torch.int64),
+    "attention_mask": torch.ones(1, max_seq_length, dtype=torch.int64),
+}
+
+symbolic_names = {0: 'batch_size', 1: 'max_seq_len'}
+
+torch.onnx.export(
+    model,args=tuple(inputs.values()),
+    f="model.onnx",
+    export_params=True, 
+    input_names=["input_ids", "attention_mask"], output_names=["last_hidden_state"],
+    dynamic_axes={"input_ids": symbolic_names, "attention_mask": symbolic_names}
+)

all-mpnet-base-v2-onnx/handler.py

@@ -0,0 +1,40 @@
+from typing import  Dict, List, Any
+
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+class EndpointHandler():
+
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = ORTModelForFeatureExtraction.from_pretrained(path, file_name="model.onnx")
+        
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`list`:. The list contains the embeddings of the inference inputs
+        """
+        inputs = data.get("inputs", data)
+
+        # tokenize the input
+        encoded_inputs = self.tokenizer(inputs, padding=True, truncation=True, return_tensors='pt')
+        # run the model
+        outputs = self.model(**encoded_inputs)
+        # Perform pooling
+        sentence_embeddings = mean_pooling(outputs, encoded_inputs['attention_mask'])
+        # Normalize embeddings
+        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+        # postprocess the prediction
+        return {"embeddings": sentence_embeddings.tolist()}

all-mpnet-base-v2-onnx/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:91c3b3d55e18d17ed4657d4cc9207940ae14b526caa9ad79e55ae90cdc6f08ec
+size 438158583

all-mpnet-base-v2-onnx/requirements.txt

@@ -0,0 +1,3 @@
+optimum[onnxruntime]
+mkl-include
+mkl

all-mpnet-base-v2-onnx/source.txt

@@ -0,0 +1 @@
+https://huggingface.co/yilunzhang/all-mpnet-base-v2-onnx

all-mpnet-base-v2-onnx/special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

all-mpnet-base-v2-onnx/tokenizer_config.json

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+{"do_lower_case": true, "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "[UNK]", "pad_token": "<pad>", "mask_token": "<mask>", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "microsoft/mpnet-base", "tokenizer_class": "MPNetTokenizer"}